Why Are CPGs Still Making Multi-Billion-Dollar Decisions Using Spreadsheets?

Many CPGs still rely on the trusted yet limited capabilities of spreadsheets as primary tools for assessing and taking action on assortment, trade spending, space and promotions planning. While effective for certain applications, spreadsheets were invented in 1979; Excel was invented in 1985. These are not intuitive or enabled tools that can provide the timely, precise details required to make multibillion dollar decisions. With the retail landscape moving faster than ever, it is time to break free from the constraints of spreadsheets and leverage the transformative power of 21^st century technologies. The future belongs to those who embrace innovation and adapt to the evolving industry landscape.

Limitations of Spreadsheets

Relatively easy to learn and use, spreadsheets are a popular option for conducting data analysis among CPGs. They offer a familiar and accessible interface for handling data, performing calculations, and creating visualizations. However, when it comes to fast decision-making in the dynamic world of consumer brands, spreadsheets reveal their limitations. While they can handle considerable amounts of data, they can be slow and unstable, particularly when data is complex. Spreadsheets further struggle to efficiently process and consolidate diverse data sets, leading to manual efforts (heavily reliant on already limited human resources) and potential inconsistencies. Excel can also impede collaboration and sharing at a time when there is more data than ever before to leverage. The bottom line is that spreadsheets are not intuitive and they require human intervention for use and to create value.

Market Volatility

Organizations are constantly trying to evaluate market volatility, competition, emerging markets and channels, and consumer behavioral shifts to assess where to allocate resources. Not having the right products and package sizes in the right place at the right time with the right price results in lost sales opportunities. If performance data shows gaps to targeted objectives, the organization will spend the year working to re-assess remaining planned actions and investments. This makes dependence on historic data troublesome. The gap between the “look back” and the “look forward” is a missed opportunity, especially in light of the market and supply chain volatility of the past three years in the CPG industry in particular.

Modern Approaches          

Today, purpose-built CPG-tailored software can ingest billions of data points from disparate sources to assess category maturity, predict future performance and assess the value of investments, allowing brands to appropriately allocate resources. It can also make more precise financial predictions. If resources are not allocated properly, expected results are not achieved. The resulting “gaps” can take a long time to close. Spreadsheets simply indicate what those gaps are; they do not indicate how to solve them. They can only hold data.

CPG-tailored technology uses timely data to project into the future, reducing dependence on historical data alone. Unlike spreadsheets, CPG focused software can “learn” from repetitive patterns and algorithms; it does not simply report data.

CPG-specific software with modeling capabilities uses multiple data sources in real time, incorporating everything from product sales and gas prices to labor department data and demographics. Because their models (accelerated by different prediction, product and pricing engines), are continuously finding data points and learning, they are able to provide forward-looking and prescriptive insights. It can signal package optimizations–e.g. whether there should be more gallon sizes of milk in a particular store versus single-serve cartons. The technology also finds those “needles in the haystack” that can be key differentiators from one store’s assortment to the next. By allowing all data to work together, teams can respond swiftly to market changes and adapt strategies dynamically, providing a competitive edge in a fast-paced industry.

Collaboration & Pinpointed Goals

Moving beyond spreadsheets enables greater collaboration and agility. Cloud-based platforms and data-sharing technologies have begun to facilitate seamless communication across departments, breaking down silos and fostering a collaborative culture. As part of that evolution, good software can facilitate better annual business planning, factoring in supply chain, labor and other costs into input assumption fields. The beauty of this is that it gives visibility to everyone in an organization and makes highly accurate predictions. This elevates target-setting, breaking out targets by function. It measures and compares achievements and lets retailers and suppliers work together to meet goals. Retailers and CPGs can then enable the Joint Business Planning process with these same powerful tools and more collaboratively agree upon a set of metrics and activities that will achieve aligned business objectives that are very specific to categories, investments or activities. Progress against all objectives is part of the modeling, constantly assessing and improving accuracy of predictions, reducing or eliminating the replanning that results from gap closure and volatility.  

Lack of Trust & Familiarity with AI

Mainstream media and technology companies have made the topic of AI so confusing to the point that it now seems too conceptual and risky to adopt. Despite evidence to support the use of AI, its effective application to broad data sources and existing processes is still nascent in the CPG industry. Just 11% of CPG organizations have adopted ML/AI tools. This stems from various factors, including concerns about the accuracy and reliability of AI algorithms, and a lack of clarity on how to apply the forms and functions of AI models to existing business processes.

There is tremendous efficiency to be gained using technology over spreadsheet, regardless of whether it incorporates a little AI or a lot of AI. Good software does not necessitate adding people (nor replacing people) to make that happen. It’s a small investment compared to what the returns can be when technology is used to augment teams and enable them to act with exponential speed and precision. Any returns can be high with clearly measurable objective-setting and ROI.     

Conclusion

The move away from spreadsheets is not just a call for change; it is an opportunity for growth and innovation. By embracing cutting-edge software and analytics, the full potential of data can be unlocked, allowing CPGs to make informed decisions and drive sustainable business growth. The time to act is now, as the CPG landscape continues to evolve rapidly. Those who adapt to change will be the ones to thrive and capitalize on the transformation opportunity.

To learn how you can evolve to be a more agile and AI enabled company, contact Insite AI.

How CPGs Can Move Beyond Price (Featured on Path to Purchase)

Guest article originally featured on Path to Purchase Institute website. See original article here.

Amid the current inflation cooldown, retailers and consumers are over price hikes. It’s now on brands to implement strategies that drive organic volume growth. Retailers are seeking brand partners with knowledge and data that lifts a total category and moves products. Consumers want prices back to normal.

About the Author: Brooke Hodierne currently serves as an EVP – strategy consulting at Insite AI, an AI and strategy partner for larger consumer brands. She joined the company following her time as SVP of merchandising for 7-Eleven. In the role, she drove category management teams that developed, implemented and communicated merchandising strategies for vault, packaged goods, tobacco and services.

Before joining 7-Eleven, Brooke held multiple positions at Giant Eagle, serving as VP of own brands, senior director of strategic sourcing and own brands, and director of prepared foods merchandising. She supported brand marketing at Del Monte Foods and held analytical roles with financial investment firms Wilshire Associates, Federated Investors and the Vanguard Group.

Weathering Retail M&A: How CPGs Can Ride the Waves With AI (Featured on CSP Daily)

Guest commentary featured on CSP Daily News. See full article.

With AI, CPGs can weather the storm and gain some control during the stressful M&A process. CPGs can use AI and bring thoughtful insights to the table that ease any tension in the process and give them more control at the same time. CPGs can look to AI to support difficult conversations and arm the newly formed retailer with accurate predictions around store space, total units, unique demand, loyalty and more.

About the Author: 
Brooke Hodierne currently serves as an EVP – strategy consulting at Insite AI, an AI and strategy partner for larger consumer brands. She joined the company following her time as SVP of merchandising for 7-Eleven. In the role, she drove category management teams that developed, implemented and communicated merchandising strategies for vault, packaged goods, tobacco and services.

Before joining 7-Eleven, Brooke held multiple positions at Giant Eagle, serving as VP of own brands, senior director of strategic sourcing and own brands, and director of prepared foods merchandising. She supported brand marketing at Del Monte Foods and held analytical roles with financial investment firms Wilshire Associates, Federated Investors and the Vanguard Group.

How AI Will Revolutionize Annual Business Planning

Annual business planning is one of those constants, like taxes and change, that nearly every organization can count on each year. It is enormously important to consumer goods organizations, and is a complex and ongoing process throughout a fiscal year where brands continuously shift priorities and strategies to meet performance gaps and adjust to fluctuating business conditions.

And this is all still largely done on spreadsheets.

Planning tool evolution (or lack thereof) aside, CPG organizations typically inform their annual planning decisions with historical sales trends and year-over-year performance data to paint a predictive view of how the year ahead might play out.

It is a strategy built on looking backward to go forward. This model has been reliable; learning from history has always been a competency, rather than a liability, and the consumer goods industry has typically been one of stability and predictability. However, history also tells us what worked before is not always going to be what works going forward (just ask Blockbuster Video).

CPGs (as most of us do) often miss black swan events, those rare sea changes in the market, because they are repeating what was done before. In our current environment of ever-advancing artificial intelligence and machine learning capabilities, we can now more accurately look ahead, better preparing brands for what may seem unpredictable. Further, the benefit of AI is continuous learning and an ongoing, realistic view of the direction in which a brand’s portfolio is heading, providing predictive outcomes against which to work and to plan.

The application of AI to annual business planning is a tipping point in organizations’ operations, resourcing, and capabilities. With smarter, evolved predictive market analytics, CPGs can lead the market in making the annual business planning process more manageable, and more importantly, more accurate.

It All Begins With Reliable and Relevant Data

The last few years may have produced some of the most historically unreliable data on consumer behavior. The COVID-19 pandemic, inflation and record-high costs resulted in brands facing highly unpredictable situations. Across the board, supply, labor, health, and macroeconomic trends created one hurdle after another for the production and delivery of goods of any kind.

When it comes to annual business planning, brands working backward to look forward aren’t fully armed to make the best decisions about what part of history will repeat itself. AI-powered predictive analytics integrate multiple sources of data, stabilizing volatility and creating a continuous learning model, enabling it to constantly import new data, test, learn and readjust to only deliver the most relevant information.

Produce Actual Insights on Category Futures

AI capabilities, when applied to annual planning, shift mindsets on portfolio investments. With predictive analytics at its heart, the future performance of categories and product classes/packs informs the most appropriate growth targets and levels of investment, optimizing profitability and effort. Imagine the efficiencies that could be attained through knowing, before hindsight is available, which categories are shifting in maturity? The cycle of growth and decline in any category (and the creation of new categories), based on consumer behavior and sentiment, is the moving target within which brands bet on growth investments and performance, all of which begins with the annual business planning process.

• Emerging / Growth categories. These categories are where new entrants, or even evolving established products, begin defining new niches within an existing category. At one time, ‘energy’ was not a category, but is now one of the largest categories in any cold vault, with most trend data pointing to continued growth ahead. Winning in newly defined space is both potentially a higher risk and a bigger reward. This is a category that will see many new competitors enter the category, but there is a big growth potential, and AI can help brands identify where to invest and take advantage of the white space in the market.
  
  
• Mature categories. These more developed categories face limited incremental space availability and more competition within existing space. But small amounts of growth in these categories can be worth more dollars in totality, since household penetration is likely higher in a mature category. Here, AI can enable brands to appropriately optimize strategic goals and investments to maximize potential.
  
  
• Declining categories. In these categories, space is often shifted to emerging categories as a result of sustained declines overall. Which is not to say that a category will eventually be eliminated, but sized appropriately, it could eventually evolve into a growth category with new entrants and evolution of offerings. AI can help brands optimize portfolios, but the technology can also help identify how to disrupt a declining category to bring back growth trends.

Shifting from Setting Targets to Closing Gaps

Annual business planning is just getting started once the targets are set. This continuous cycle on which nearly all business routines are anchored is one of measuring progress and performance against targets and plans, closing gaps, adjusting strategies and solving challenges that arise. AI can quickly help teams optimize strategies to focus on the best opportunities to shift resources and priorities to achieve plan goals. Further, if teams are using AI continuously in this process throughout the year and make it an ongoing part of reporting and performance measurement, trends could be better predictive and prescriptive analytics can used to take the most efficient and effective action possible.

AI Is Annual Business Planning

It’s important to note that AI doesn’t remove the human in the middle of the data. AI helps find the most impactful needles in the haystack for teams to consider and around which to develop strategies.

AI/ML never stops learning, so organizations and teams can be prepared for fluctuations and changes in near real-time, removing inefficiency in guesswork, creating options for action, and ultimately, enabling plan achievement. At its core, AI technology is annual business planning. Customized solutions are designed to look at where a brand / organization is sitting relative to the category and market, identify where the consumer / trends will go, harmonize data streams to inform financial deliverables, and then manage to and against those targets in aggregate through continuous learning.

Put the Spreadsheets Away

Establish leadership in the industry by shifting the paradigm on annual business planning. Free up resources currently mired in planning and re-planning to get back to the business of thought leadership. Take advantage of what innovative technologies offer and evolve dynamically beyond the complexity of a static spreadsheet. Enabling the future means finding better ways to work smarter: the thoughtful application of AI in your data environment is the best way to do that now.

To learn more about how AI can create efficiencies in resources and accuracy in both macro and micro-trend planning, click here.

Be the Smartest CPG in the Room During Joint Business Planning (Featured on Consumer Goods Technology)

Guest article originally featured on Consumer Goods Technology. See full article.

Joint business planning is the lifeblood of a brand’s success at a retailer.  During these meetings, retailers are looking to CPGs to bring them deep insights and category stories.

Brands should come to retailers with truly powerful insights that more accurately predict how categories will perform in the future, assist retailer partners to make intelligent decisions and advance the outcomes of joint business planning meetings. Machine learning, AI and predictive analytics can help CPGs ultimately create advantage for themselves and the retailer.

About the Author: 
Brooke Hodierne currently serves as an EVP – strategy consulting at Insite AI, an AI and strategy partner for larger consumer brands. She joined the company following her time as SVP of merchandising for 7-Eleven. In the role, she drove category management teams that developed, implemented and communicated merchandising strategies for vault, packaged goods, tobacco and services.

Before joining 7-Eleven, Brooke held multiple positions at Giant Eagle, serving as VP of own brands, senior director of strategic sourcing and own brands, and director of prepared foods merchandising. She supported brand marketing at Del Monte Foods and held analytical roles with financial investment firms Wilshire Associates, Federated Investors and the Vanguard Group.